{"id":"W3125334212","doi":"10.17705/1jais.00237","title":"A Theory-Driven Design Framework for Social Recommender Systems","year":2010,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada; City University of New York","keywords":"Recommender system; Computer science; Competence (human resources); Similarity (geometry); Designtheory; Design science; Artificial intelligence; Information retrieval; Knowledge management; Human–computer interaction; Psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005134041,0.0001207022,0.0003205136,0.0002106754,0.0004017367,0.0006248638,0.001030206,0.0002368114,6.557469e-7],"category_scores_gemma":[0.001942069,0.00008434384,0.0003467242,0.0003430661,0.00001227874,0.002450791,0.00006165054,0.0003300897,0.000005477131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003901857,"about_ca_system_score_gemma":0.0001177889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001837401,"about_ca_topic_score_gemma":5.597698e-7,"domain_scores_codex":[0.9978777,0.0002346473,0.001023315,0.0000743139,0.0005775643,0.0002124949],"domain_scores_gemma":[0.9925618,0.001356831,0.004141179,0.0002617499,0.001631636,0.00004676392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003098027,0.00002208545,0.0002518112,0.00004931197,0.0002135562,3.397823e-8,0.002300001,0.00440332,0.0001885555,0.9679269,0.02227327,0.00234023],"study_design_scores_gemma":[0.002212572,0.000305587,0.0007432893,0.00022749,0.000251802,0.00004993411,0.001749121,0.2362011,0.001769832,0.2248732,0.5310254,0.0005907014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000163006,0.00001685601,0.9941766,0.00113664,0.003243557,0.0008554814,0.00001637985,0.00006545175,0.000325982],"genre_scores_gemma":[0.8218882,0.000005290149,0.1762631,0.000318496,0.0008711318,0.0001848867,0.0000045321,0.00001465718,0.000449692],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8217252,"threshold_uncertainty_score":0.6025575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02622215138912869,"score_gpt":0.3018622921912353,"score_spread":0.2756401408021066,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}